Hi, I want to analyze a mixed model for repeated measurements at months 1,3,6,9,12,18,24. Treatments are active1 active2, and placebo. The model will be adjusted for the 3 quantitative (baseline, age, duration of disease) and 3 dichotomous factors. Month is intentionally modeled as a categorical factor. PROC MIXED ORDER=internal; CLASS patid trt month covar1 covar2 covar3; MODEL y= month age disdur trt trt*month baseline baseline*month covar1 covar1*month covar2 covar2*month covar3 covar3*month / S DDFM=KR; REPEATED month / TYPE=un SUBJECT=patid; LSMEANS trt*month covar1*month covar2*month covar3*month/ CL DIFF E; I need to estimate LSM (s.e.) for the 3 treatment groups at Months 12 and Month 24. This I can do with a simple LSMEANS statement. Question: How can I estimate the average LSM (s.e.) of the 2 active treatments? The only way I can think of is to use the 'E' option in the LSMEANS statement to save the all covariates' coefficients used for constructing least square means, write the coefficients into macro variables, and then run the model again with a fairly complex ESTIMATE statement to derive the LSM of the pooled active treatments. Does anybody have a simpler solution?
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